Abstract. We propose HyDICE, Hybrid DIscrete Continuous Exploration, a multi-layered approach for hybrid-system testing that integrates continuous sampling-based robot motion planning with discrete searching. The discrete search uses the discrete transitions of the hybrid system and coarse-grained decompositions of the continuous state spaces or related projections to guide the motion planner during the search for witness trajectories. Experiments presented in this paper, using a hybrid system inspired by robot motion planning and with nonlinear dynamics associated with each of several thousand modes, provide an initial validation of HyDICE and demonstrate its promise as a hybrid-system testing method. Comparisons to related work show computational speedups of up to two orders of magnitude.

Abstract — Computationally efficient motion planning must avoid exhaustive exploration of high-dimensional configuration spaces by leveraging the structure present in real-world planning problems. We argue that this can be accomplished most effectively by carefully balancing exploration and exploitation. Exploration seeks to understand configuration space, irrespective of the planning problem, and exploitation acts to solve the problem, given the available information obtained by exploration. We present an exploring/exploiting tree (EET) planner that balances its exploration and exploitation behavior. The planner acquires workspace information and subsequently uses this information for exploitation in configuration space. If exploitation fails in difficult regions the planner gradually shifts to its behavior towards exploration. We present experimental results to demonstrate that adaptive balancing of exploration and exploitation leads to significant performance improvements, compared to other state-of-the-art sampling-based planners. I.

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Abstract — We have recently proposed DSLX, a motion planner that significantly reduces the computational time for solving challenging kinodynamic problems by interleaving continuous state-space exploration with discrete search on a workspace decomposition. An important but inadequately understood aspect of DSLX is the role of the workspace decomposition on the computational efficiency of the planner. Understanding this role is important for successful applications of DSLX to increasingly complex robotic systems. This work shows that the granularity of the workspace decomposition directly impacts computational efficiency: DSLX is faster when the decomposition is neither too fine- nor too coarse-grained. Finding the right level of granularity can require extensive fine-tuning. This work demonstrates that significant computational efficiency can instead be obtained with no fine-tuning by using conforming Delaunay triangulations, which in the context of DSLX provide a natural workspace decomposition that allows an efficient interplay between continuous state-space exploration and discrete search. The results of this work are based on extensive experiments on DSLX using grid, trapezoidal, and triangular decompositions of various granularities to solve challenging first and second-order kinodynamic motion-planning problems. I.

Abstract — Autonomous navigation in unknown but wellstructured environments (e.g., parking lots) is a common task for human drivers and an important goal for autonomous vehicles. In such environments, the vehicles must obey the standard conventions of driving (e.g., passing oncoming vehicles on the correct side), but often lack a map that can be used to guide path planning in an appropriate way. The robots must therefore rely on features of the environment to drive in a safe and predictable way. In this work, we focus on detecting one of such features, the principal directions of the environment. We propose a Markov-random-field (MRF) model for estimating the maximum-likelihood field of principal directions, given the local linear features extracted from the vehicle’s sensor data, and show that the method leads to robust estimates of principal directions in complex real-life driving environments. We also demonstrate how the computed principal directions can be used to guide a path-planning algorithm, leading to the generation of significantly improved trajectories. I.

We describe a practical path-planning algorithm that generates smooth paths for an autonomous vehicle operating in an unknown environment, where obstacles are detected online by the robot’s sensors. This work was motivated by and experimentally validated in the 2007 DARPA Urban Challenge, where robotic vehicles had to autonomously navigate parking lots. Our approach has two main steps. The first step uses a variant of the well-known A * search algorithm, applied to the 3D kinematic state space of the vehicle, but with a modified state-update rule that captures the continuous state of the vehicle in the discrete nodes of A * (thus guaranteeing kinematic feasibility of the path). The second step then improves the quality of the solution via numeric non-linear optimization, leading to a local (and frequently global) optimum. The

Abstract — Existing sampling-based robot motion planning methods are often inefficient at finding trajectories for kinodynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines elements from several prior approaches and may incorporate a cost model based on the LQG-MP framework to estimate the probability of collision under uncertainty in control and sensing. We compare the performance of EG-RRT with several prior approaches on challenging sample problems. Results suggest that EG-RRT offers significant improvements in performance. I.

Abstract. This paper develops a novel computational method for the falsification of safety properties specified by syntactically safe linear temporal logic (LTL) formulas φ for hybrid systems with general nonlinear dynamics and input controls. The method is based on an effective combination of robot motion planning and model checking. Experiments on a hybrid robotic system benchmark with nonlinear dynamics show significant speedup over related work. The experiments also indicate significant speedup when using minimized DFA instead of non-minimized NFA, as obtained by standard tools, for representing the violating prefixes of φ. 1

Abstract—This paper proposes a robotics-inspired method to enhance sampling of native-like protein conformations when employing only amino-acid sequence. Computing such conformations, essential to associate structural and functional information withgenesequences,ischallengingduetothehigh-dimensionality and the rugged energy surface of the protein conformational space. The contribution of this work is a novel two-layered method to enhance the sampling of geometrically-distinct lowenergy conformations at a coarse-grained level of detail. The method grows a tree in conformational space reconciling two goals:(i)guidingthetreetowardslowerenergiesand(ii)notoversampling geometrically-similar conformations. Discretizations of the energy surface and a low-dimensional projection space are employed to select more often for expansion low-energy conformations in under-explored regions of the conformational space. The tree is expanded with low-energy conformations through a Metropolis Monte Carlo framework that uses a move set of physical fragment configurations. Testing on sequences of seven small-to-medium structurally-diverse proteins shows that the method rapidly samples native-like conformations in a few hours on a single CPU. Analysis shows that computed conformations are good candidates for further detailed energetic refinements by larger studies in protein engineering and design. I.

Science Mission enabled by (or benefiting from) on-orbit servicing or assembly: o HST, JWST, Starshade spacecraft, ATLAST Facilities to enable telescope technology using the ISS Enabling Technologies for on-orbit servicing: o Software environment for remote manipulation with time delay Format: White Paper All organizations listed above are willing to a) participate and present our ideas at the follow-up workshop, b) showcase our capabilities to the NASA study team at our locations, and c) engage in and contribute to developing/enhancing technologies for onorbit servicing both with NASA and with other partner organizations. JPL has controlled information that might be useful for this exercise and would be willing